Optimizing Currency Circulation Forecasts in Indonesia: A Hybrid Prophet- Long Short Term Memory Model with Hyperparameter Tuning
Keywords:
Currency Circulation, Forecasting, Hybrid Model, Long Short Term Memory, Prophet
Abstract
The core problem for decision-makers lies in selecting an effective forecasting method, particularly when faced with the challenges of nonlinearity and nonstationarity in time series data. To address this, hybrid models are increasingly employed to enhance forecasting accuracy. In Indonesia and other Muslim countries, monthly economic and business time series data often include trends, seasonality, and calendar variations. This study compares the performance of the hybrid Prophet-Long Short-Term Memory (LSTM) model with their individual counterparts to forecast such patterned time series. The aim is to identify the best model through a hybrid approach for forecasting time series data exhibiting
trend, seasonality, and calendar variations, using the real-life case of currency circulation in South Sulawesi. The goodness of the models is evaluated using the smallest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The results indicate that the hybrid Prophet- LSTM model demonstrates superior accuracy, especially for predicting currency outflow, with lower MAPE and RMSE values than standalone models. The LSTM model shows excellent performance for currency inflow, while the Prophet model lags in inflow and outflow accuracy. This insight is valuable for Bank Indonesia’s strategic planning, aiding in better cash flow prediction and currency stock management.
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References
[1] C. Kuranga, N. Ranganai, and T. S. Muwani, “Particle swarm optimization-based empirical mode decomposition predictive
technique for nonstationary data,” The Journal of Supercomputing, vol. 78, no. 18, pp. 19 662–19 683, dec 2022,
https://doi.org/10.1007/s11227-022-04646-6.
[2] Z. F. Maghfiroh, Suhartono, H. Prabowo, N. A. Salehah, D. D. Prastyo, and Setiawan, “Forecasting Inflow and Outflow of
Currency in Central Java using ARIMAX, RBFN and Hybrid ARIMAX-RBFN,” Journal of Physics: Conference Series, vol.
1863, no. 1, p. 012066, mar 2021, https://doi.org/10.1088/1742-6596/1863/1/012066.
[3] D. D. Prastyo, S. Suhartono, A. O. B. Puka, and M. H. Lee, “Comparison Between Hybrid Quantile Regression Neural
Network and Autoregressive Integrated Moving Average With Exogenous Variable for Forecasting of Currency Inflow and
Outflow in Bank Indonesia,” Jurnal Teknologi, vol. 80, no. 6, aug 2018, https://doi.org/10.11113/jt.v80.11785.
[4] A. Susanti, Suhartono, H. J. Setyadi, M. Taruk, Haviluddin, and P. P. Widagdo, “Forecasting Inflow and Outflow of Money
Currency in East Java Using a Hybrid Exponential Smoothing and Calendar Variation Model,” Journal of Physics: Conference
Series, vol. 979, p. 012096, mar 2018, https://doi.org/10.1088/1742-6596/979/1/012096.
[5] J. P. L. Deepak, Y. R. Subramanian, J. J. Lalitha, and K. Vidhya, “Optimum Level of Currency Reserves:
Investigation and Forecasting of Indian Rupee Using ARIMA Model,” Journal of Business Cycle Research, feb 2024,
https://doi.org/10.1007/s41549-023-00091-3.
[6] M. Monica and A. Suharsono, “Forecasting cash outflow and inflow in Jember with ARIMAX calendar variation effect,” 2021,
p. 020014, https://doi.org/10.1063/5.0039283.
[7] D. T. D, D. P. Bung, and D. J. M, “Predicting exchange rate between the us dollar (USD) and Indian rupee (INR): An empirical
analysis using Sarima model,” International Journal of Research in Finance and Management, vol. 7, no. 1, pp. 56–63, jan
2024, https://doi.org/10.33545/26175754.2024.v7.i1a.283.
[8] S. Akhtar, M. Ramzan, S. Shah, I. Ahmad, M. I. Khan, S. Ahmad, M. A. El-Affendi, and H. Qureshi, “Forecasting Exchange
Rate of Pakistan Using Time Series Analysis,” Mathematical Problems in Engineering, vol. 2022, pp. 1–11, aug 2022,
https://doi.org/10.1155/2022/9108580.
[9] S. B. Atoyebi, M. F. Olayiwola, J. O. Oladapo, and D. I. Oladapo, “Forecasting Currency in Circulation in Nigeria Using
Holt-Winters Exponential Smoothing Method,” South Asian Journal of Social Studies and Economics, vol. 20, no. 1, pp.
25–41, jul 2023, https://doi.org/10.9734/sajsse/2023/v20i1689.
[10] S. Suhartono, D. E. Ashari, D. D. Prastyo, H. Kuswanto, and M. H. Lee, “Deep Neural Network for Forecasting Inflow and
Outflow in Indonesia,” Sains Malaysiana, vol. 48, no. 8, pp. 1787–1798, aug 2019, https://doi.org/10.17576/jsm-2019-4808-26.
[11] A. Biswas, I. A. Uday, K. M. Rahat, M. S. Akter, and M. R. C. Mahdy, “Forecasting the United State Dollar(USD)/Bangladeshi
Taka (BDT) exchange rate with deep learning models: Inclusion of macroeconomic factors influencing the currency exchange
rates,” PLOS ONE, vol. 18, no. 2, p. e0279602, feb 2023, https://doi.org/10.1371/journal.pone.0279602.
[12] J.-S. Lee, Y.-L. Yong, M.-H. Hoo, and K.-C. Khor, “A Comparison of Machine Learning Models for Currency Pair
Forecasting,” 2023, pp. 499–506, https://doi.org/10.1007/978-3-031-19958-5 47.
[13] V. D. Derbentsev, V. S. Bezkorovainyi, and I. V. Luniak, “Application of Deep Learning Methods to Forecasting Changes in
Short-Term Currency Trends,” Scientific Bulletin of Mukachevo State University Series “Economics”, vol. 7, no. 2, pp. 75–86,
dec 2020, https://doi.org/10.52566/msu-econ.7(2).2020.75-86.
[14] F. Raheem and N. Iqbal, “Forecasting foreign exchange rate: Use of FbProphet,” in 2021 International Research Conference
on Smart Computing and Systems Engineering (SCSE). IEEE, sep 2021, pp. 44–48, https://doi.org/10.1109/SCSE53661.
2021.9568284.
[15] S. V. Bayani, I. A. Mohamed, and S. Venkatasubbu, “Robustness and Interpretability of Machine Learning Models in Financial
Forecasting,” European Journal of Technology, vol. 8, no. 2, pp. 54–66, may 2024, https://doi.org/10.47672/ejt.2005.
[16] K. E. Hoque and H. Aljamaan, “Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting,”
IEEE Access, vol. 9, pp. 163 815–163 830, 2021, https://doi.org/10.1109/ACCESS.2021.3134138.
[17] S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 Competition: Results, findings, conclusion and way forward,”
International Journal of Forecasting, vol. 34, no. 4, pp. 802–808, oct 2018, https://doi.org/10.1016/j.ijforecast.2018.06.001.
[18] Z. Hajirahimi and M. Khashei, “An Optimal Hybrid Bi-Component Series-Parallel Structure for Time Series
Forecasting,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11 067–11 078, nov 2023,
https://doi.org/10.1109/TKDE.2022.3231008.
[19] S. J. Taylor and B. Letham, “Forecasting at Scale,” The American Statistician, vol. 72, no. 1, pp. 37–45, jan 2018,
https://doi.org/10.1080/00031305.2017.1380080.
[20] F. M. Salem, “Gated RNN: The Long Short-Term Memory (LSTM) RNN,” in Recurrent Neural Networks. Cham: Springer
International Publishing, 2022, pp. 71–82, https://doi.org/10.1007/978-3-030-89929-5 4.
[21] B. Indonesia, “Payment System and Financial Market Infrastructure Statistics (PSFMI) DECEMBER 2023,” 2024.
[22] Z. Chen, C. Wang, L. Lv, L. Fan, S. Wen, and Z. Xiang, “Research on Peak Load Prediction of Distribution Network Lines
Based on Prophet-LSTM Model,” Sustainability, vol. 15, no. 15, p. 11667, jul 2023, https://doi.org/10.3390/su151511667.
[23] S. Arslan, “A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series
data,” PeerJ Computer Science, vol. 8, p. e1001, jun 2022, https://doi.org/10.7717/peerj-cs.1001.
[24] V. Nur Aziza, F. H. Moh’d, F. A. Maghfiroh, K. A. Notodiputro, and Y. Angraini, “Performance Comparison
of Sarima Intervention and Prophet Models for Forecasting the Number of Airline Passenger at Soekarno-Hatta
International Airport,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 4, pp. 2107–2120, dec 2023,
https://doi.org/10.30598/barekengvol17iss4pp2107-2120.
[25] Y. Ensafi, S. H. Amin, G. Zhang, and B. Shah, “Time-series forecasting of seasonal items sales using machine learning – A
comparative analysis,” International Journal of Information Management Data Insights, vol. 2, no. 1, p. 100058, apr 2022,
https://doi.org/10.1016/j.jjimei.2022.100058.
[26] M. Ula, V. Ilhadi, and Z. M. Sidek, “Comparing Long Short-Term Memory and Random Forest Accuracy for Bitcoin Price
Forecasting,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 2, pp. 259–272, jan
2024, https://doi.org/10.30812/matrik.v23i2.3267.
[27] I. N. G. A. Astawa, I. P. B. A. Pradnyana, and I. K. Suwintana, “Comparison of RNN, LSTM, and GRU Methods on
Forecasting Website Visitors,” Journal of Computer Science and Technology Studies, vol. 4, no. 2, pp. 11–18, aug 2022,
https://doi.org/10.32996/jcsts.2022.4.2.3.
[28] K. Eckhardt, “Choosing the right Hyperparameters for a simple LSTM using Keras.”
[29] K. Chowdhury, “10 Hyperparameters to keep an eye on for your LSTM model— and other tips.”
[30] J. Brownlee, “How to Tune LSTM Hyperparameters with Keras for Time Series Forecasting.”
[31] L. Zhoul, M. Chenl, and Q. Ni, in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), https://doi.org/10.1109/
SSCI47803.2020.9308543, isbn = 978-1-7281-2547-3, mendeley-groups = [MATRIK Vol 24 No 1] Vivin Nur Aziza, month
= dec, pages = 595–601, publisher = IEEE, title = A hybrid Prophet-LSTM Model for Prediction of Air Quality Index, url =
https://ieeexplore.ieee.org/document/9308543/, year = 2020.
[32] R. N. M. Sanusi, B. Susetyo, and U. D. Syafitri, “Simulation of the Sarima Model With Three-Way Anova and Its Application
in Forecasting Large Chillies Prices in Five Provinces on Java Island,” BAREKENG: Jurnal Ilmu Matematika dan Terapan,
vol. 17, no. 1, pp. 0253–0262, apr 2023, https://doi.org/10.30598/barekengvol17iss1pp0253-0262.
[33] M. D. Kartikasari, “Forecasting of Currency Circulation in Indonesia Using Hybrid Extreme Learning Machine,”
BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 16, no. 2, pp. 635–642, jun 2022, https://doi.org/10.30598/
barekengvol16iss2pp635-642.
[34] O. Ozdemir and C. Yozgatligil, “Forecasting performance of machine learning, time series, and hybrid methods for low- and
high-frequency time series,” Statistica Neerlandica, vol. 78, no. 2, pp. 441–474, may 2024, https://doi.org/10.1111/stan.12326.
[35] J. A. Putri, S. Suhartono, H. Prabowo, N. A. Salehah, D. D. Prastyo, and S. Setiawan, “Forecasting Currency in East Java:
Classical Time Series vs. Machine Learning,” Indonesian Journal of Statistics and Its Applications, vol. 5, no. 2, pp. 284–303,
jun 2021, https://doi.org/10.29244/ijsa.v5i2p284-303.
[36] M. Monica, A. Suharsono, B. W. Otok, and A. Wibisono, “Hybrid SARIMA-FFNN model in forecasting
cash outflow and inflow,” Journal of Physics: Conference Series, vol. 2106, no. 1, p. 012002, nov 2021,
https://doi.org/10.1088/1742-6596/2106/1/012002.
technique for nonstationary data,” The Journal of Supercomputing, vol. 78, no. 18, pp. 19 662–19 683, dec 2022,
https://doi.org/10.1007/s11227-022-04646-6.
[2] Z. F. Maghfiroh, Suhartono, H. Prabowo, N. A. Salehah, D. D. Prastyo, and Setiawan, “Forecasting Inflow and Outflow of
Currency in Central Java using ARIMAX, RBFN and Hybrid ARIMAX-RBFN,” Journal of Physics: Conference Series, vol.
1863, no. 1, p. 012066, mar 2021, https://doi.org/10.1088/1742-6596/1863/1/012066.
[3] D. D. Prastyo, S. Suhartono, A. O. B. Puka, and M. H. Lee, “Comparison Between Hybrid Quantile Regression Neural
Network and Autoregressive Integrated Moving Average With Exogenous Variable for Forecasting of Currency Inflow and
Outflow in Bank Indonesia,” Jurnal Teknologi, vol. 80, no. 6, aug 2018, https://doi.org/10.11113/jt.v80.11785.
[4] A. Susanti, Suhartono, H. J. Setyadi, M. Taruk, Haviluddin, and P. P. Widagdo, “Forecasting Inflow and Outflow of Money
Currency in East Java Using a Hybrid Exponential Smoothing and Calendar Variation Model,” Journal of Physics: Conference
Series, vol. 979, p. 012096, mar 2018, https://doi.org/10.1088/1742-6596/979/1/012096.
[5] J. P. L. Deepak, Y. R. Subramanian, J. J. Lalitha, and K. Vidhya, “Optimum Level of Currency Reserves:
Investigation and Forecasting of Indian Rupee Using ARIMA Model,” Journal of Business Cycle Research, feb 2024,
https://doi.org/10.1007/s41549-023-00091-3.
[6] M. Monica and A. Suharsono, “Forecasting cash outflow and inflow in Jember with ARIMAX calendar variation effect,” 2021,
p. 020014, https://doi.org/10.1063/5.0039283.
[7] D. T. D, D. P. Bung, and D. J. M, “Predicting exchange rate between the us dollar (USD) and Indian rupee (INR): An empirical
analysis using Sarima model,” International Journal of Research in Finance and Management, vol. 7, no. 1, pp. 56–63, jan
2024, https://doi.org/10.33545/26175754.2024.v7.i1a.283.
[8] S. Akhtar, M. Ramzan, S. Shah, I. Ahmad, M. I. Khan, S. Ahmad, M. A. El-Affendi, and H. Qureshi, “Forecasting Exchange
Rate of Pakistan Using Time Series Analysis,” Mathematical Problems in Engineering, vol. 2022, pp. 1–11, aug 2022,
https://doi.org/10.1155/2022/9108580.
[9] S. B. Atoyebi, M. F. Olayiwola, J. O. Oladapo, and D. I. Oladapo, “Forecasting Currency in Circulation in Nigeria Using
Holt-Winters Exponential Smoothing Method,” South Asian Journal of Social Studies and Economics, vol. 20, no. 1, pp.
25–41, jul 2023, https://doi.org/10.9734/sajsse/2023/v20i1689.
[10] S. Suhartono, D. E. Ashari, D. D. Prastyo, H. Kuswanto, and M. H. Lee, “Deep Neural Network for Forecasting Inflow and
Outflow in Indonesia,” Sains Malaysiana, vol. 48, no. 8, pp. 1787–1798, aug 2019, https://doi.org/10.17576/jsm-2019-4808-26.
[11] A. Biswas, I. A. Uday, K. M. Rahat, M. S. Akter, and M. R. C. Mahdy, “Forecasting the United State Dollar(USD)/Bangladeshi
Taka (BDT) exchange rate with deep learning models: Inclusion of macroeconomic factors influencing the currency exchange
rates,” PLOS ONE, vol. 18, no. 2, p. e0279602, feb 2023, https://doi.org/10.1371/journal.pone.0279602.
[12] J.-S. Lee, Y.-L. Yong, M.-H. Hoo, and K.-C. Khor, “A Comparison of Machine Learning Models for Currency Pair
Forecasting,” 2023, pp. 499–506, https://doi.org/10.1007/978-3-031-19958-5 47.
[13] V. D. Derbentsev, V. S. Bezkorovainyi, and I. V. Luniak, “Application of Deep Learning Methods to Forecasting Changes in
Short-Term Currency Trends,” Scientific Bulletin of Mukachevo State University Series “Economics”, vol. 7, no. 2, pp. 75–86,
dec 2020, https://doi.org/10.52566/msu-econ.7(2).2020.75-86.
[14] F. Raheem and N. Iqbal, “Forecasting foreign exchange rate: Use of FbProphet,” in 2021 International Research Conference
on Smart Computing and Systems Engineering (SCSE). IEEE, sep 2021, pp. 44–48, https://doi.org/10.1109/SCSE53661.
2021.9568284.
[15] S. V. Bayani, I. A. Mohamed, and S. Venkatasubbu, “Robustness and Interpretability of Machine Learning Models in Financial
Forecasting,” European Journal of Technology, vol. 8, no. 2, pp. 54–66, may 2024, https://doi.org/10.47672/ejt.2005.
[16] K. E. Hoque and H. Aljamaan, “Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting,”
IEEE Access, vol. 9, pp. 163 815–163 830, 2021, https://doi.org/10.1109/ACCESS.2021.3134138.
[17] S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 Competition: Results, findings, conclusion and way forward,”
International Journal of Forecasting, vol. 34, no. 4, pp. 802–808, oct 2018, https://doi.org/10.1016/j.ijforecast.2018.06.001.
[18] Z. Hajirahimi and M. Khashei, “An Optimal Hybrid Bi-Component Series-Parallel Structure for Time Series
Forecasting,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11 067–11 078, nov 2023,
https://doi.org/10.1109/TKDE.2022.3231008.
[19] S. J. Taylor and B. Letham, “Forecasting at Scale,” The American Statistician, vol. 72, no. 1, pp. 37–45, jan 2018,
https://doi.org/10.1080/00031305.2017.1380080.
[20] F. M. Salem, “Gated RNN: The Long Short-Term Memory (LSTM) RNN,” in Recurrent Neural Networks. Cham: Springer
International Publishing, 2022, pp. 71–82, https://doi.org/10.1007/978-3-030-89929-5 4.
[21] B. Indonesia, “Payment System and Financial Market Infrastructure Statistics (PSFMI) DECEMBER 2023,” 2024.
[22] Z. Chen, C. Wang, L. Lv, L. Fan, S. Wen, and Z. Xiang, “Research on Peak Load Prediction of Distribution Network Lines
Based on Prophet-LSTM Model,” Sustainability, vol. 15, no. 15, p. 11667, jul 2023, https://doi.org/10.3390/su151511667.
[23] S. Arslan, “A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series
data,” PeerJ Computer Science, vol. 8, p. e1001, jun 2022, https://doi.org/10.7717/peerj-cs.1001.
[24] V. Nur Aziza, F. H. Moh’d, F. A. Maghfiroh, K. A. Notodiputro, and Y. Angraini, “Performance Comparison
of Sarima Intervention and Prophet Models for Forecasting the Number of Airline Passenger at Soekarno-Hatta
International Airport,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 4, pp. 2107–2120, dec 2023,
https://doi.org/10.30598/barekengvol17iss4pp2107-2120.
[25] Y. Ensafi, S. H. Amin, G. Zhang, and B. Shah, “Time-series forecasting of seasonal items sales using machine learning – A
comparative analysis,” International Journal of Information Management Data Insights, vol. 2, no. 1, p. 100058, apr 2022,
https://doi.org/10.1016/j.jjimei.2022.100058.
[26] M. Ula, V. Ilhadi, and Z. M. Sidek, “Comparing Long Short-Term Memory and Random Forest Accuracy for Bitcoin Price
Forecasting,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 2, pp. 259–272, jan
2024, https://doi.org/10.30812/matrik.v23i2.3267.
[27] I. N. G. A. Astawa, I. P. B. A. Pradnyana, and I. K. Suwintana, “Comparison of RNN, LSTM, and GRU Methods on
Forecasting Website Visitors,” Journal of Computer Science and Technology Studies, vol. 4, no. 2, pp. 11–18, aug 2022,
https://doi.org/10.32996/jcsts.2022.4.2.3.
[28] K. Eckhardt, “Choosing the right Hyperparameters for a simple LSTM using Keras.”
[29] K. Chowdhury, “10 Hyperparameters to keep an eye on for your LSTM model— and other tips.”
[30] J. Brownlee, “How to Tune LSTM Hyperparameters with Keras for Time Series Forecasting.”
[31] L. Zhoul, M. Chenl, and Q. Ni, in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), https://doi.org/10.1109/
SSCI47803.2020.9308543, isbn = 978-1-7281-2547-3, mendeley-groups = [MATRIK Vol 24 No 1] Vivin Nur Aziza, month
= dec, pages = 595–601, publisher = IEEE, title = A hybrid Prophet-LSTM Model for Prediction of Air Quality Index, url =
https://ieeexplore.ieee.org/document/9308543/, year = 2020.
[32] R. N. M. Sanusi, B. Susetyo, and U. D. Syafitri, “Simulation of the Sarima Model With Three-Way Anova and Its Application
in Forecasting Large Chillies Prices in Five Provinces on Java Island,” BAREKENG: Jurnal Ilmu Matematika dan Terapan,
vol. 17, no. 1, pp. 0253–0262, apr 2023, https://doi.org/10.30598/barekengvol17iss1pp0253-0262.
[33] M. D. Kartikasari, “Forecasting of Currency Circulation in Indonesia Using Hybrid Extreme Learning Machine,”
BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 16, no. 2, pp. 635–642, jun 2022, https://doi.org/10.30598/
barekengvol16iss2pp635-642.
[34] O. Ozdemir and C. Yozgatligil, “Forecasting performance of machine learning, time series, and hybrid methods for low- and
high-frequency time series,” Statistica Neerlandica, vol. 78, no. 2, pp. 441–474, may 2024, https://doi.org/10.1111/stan.12326.
[35] J. A. Putri, S. Suhartono, H. Prabowo, N. A. Salehah, D. D. Prastyo, and S. Setiawan, “Forecasting Currency in East Java:
Classical Time Series vs. Machine Learning,” Indonesian Journal of Statistics and Its Applications, vol. 5, no. 2, pp. 284–303,
jun 2021, https://doi.org/10.29244/ijsa.v5i2p284-303.
[36] M. Monica, A. Suharsono, B. W. Otok, and A. Wibisono, “Hybrid SARIMA-FFNN model in forecasting
cash outflow and inflow,” Journal of Physics: Conference Series, vol. 2106, no. 1, p. 012002, nov 2021,
https://doi.org/10.1088/1742-6596/2106/1/012002.
Published
2024-11-06
How to Cite
Aziza, V., Syafitri, U., & Fitrianto, A. (2024). Optimizing Currency Circulation Forecasts in Indonesia: A Hybrid Prophet- Long Short Term Memory Model with Hyperparameter Tuning. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 24(1), 73-84. https://doi.org/https://doi.org/10.30812/matrik.v24i1.4052
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